Scene–Object Holistic Relation Network for Fine-Grained Airplane Detection

The airplane detection and fine-grained recognition in the remote sensing images are challenging due to high interclass indistinction. The subtle distinctions between classes make it difficult to accurately classify objects based purely on bounding box features without considering the broader context. However, recent studies on remote sensing object detection focuses on refining the representation of bounding boxes while ignoring holistic context knowledge in remote sensing scenarios. This letter addresses this gap by introducing the scene-object holistic relation (SOHR) network for fine-grained airplane detection. Specifically, the SOHR network distinctively exploits global scene-object context information through a novel lightweight scene context attention (SCA) module, which aggregates scene context feature and object position information. Furthermore, the object relation transformer (ORT) is designed to model interactions among all objects within the scene explicitly, thereby increasing the model performance for ambiguous hard samples. The experimental results obtained from the FAIR1M dataset demonstrate that the proposed SOHR-Net achieves a state-of-the-art detection accuracy of 56.110% mean average precision (mAP). Compared with the baseline, SOHR-Net exhibits an increase of 2.517%.